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Opinion dynamics in networks with common-neighbors-based connections

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  • Wang, Huanjing
  • Shang, Lihui

Abstract

We investigate opinion dynamics of the model in which each agent can communicate with local neighbors whose opinions are inside the bound of confidence and meanwhile selecting long-range neighbors according to a common-neighbors rule. The common-neighbors rule means that two agents sharing more neighbors have larger probability to be connected. We find that increasing communication between agents who have common friends will prolong the time needed for the system to reach a consensus state. In contrast, the long-range connections between agents sharing no friends will promote the convergence of the system. The generality of this observation is tested against different system sizes. Simulation results also show that a large number of long-range connections help the system to reach a consensus fast.

Suggested Citation

  • Wang, Huanjing & Shang, Lihui, 2015. "Opinion dynamics in networks with common-neighbors-based connections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 180-186.
    • Handle: RePEc:eee:phsmap:v:421:y:2015:i:c:p:180-186
    DOI: 10.1016/j.physa.2014.10.090
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    1. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.
    2. Zhang, Jiangbo & Hong, Yiguang, 2013. "Opinion evolution analysis for short-range and long-range Deffuant–Weisbuch models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5289-5297.
    3. Matthew O. Jackson & Benjamin Golub, 2007. "Naïve Learning in Social Networks: Convergence, Influence and Wisdom of Crowds," Working Papers 2007.64, Fondazione Eni Enrico Mattei.
    4. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    5. Liang, Haili & Yang, Yiping & Wang, Xiaofan, 2013. "Opinion dynamics in networks with heterogeneous confidence and influence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2248-2256.
    6. Guillaume Deffuant & Frederic Amblard & Gérard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-1.
    7. Serge Galam, 2008. "Sociophysics: A Review Of Galam Models," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 409-440.
    8. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
    9. Glenn Ellison & Drew Fudenberg, 1995. "Word-of-Mouth Communication and Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 93-125.
    10. Jan Lorenz, 2007. "Continuous Opinion Dynamics Under Bounded Confidence: A Survey," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(12), pages 1819-1838.
    11. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    12. Liu, Qipeng & Wang, Xiaofan, 2013. "Social learning with bounded confidence and heterogeneous agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2368-2374.
    13. Guillaume Deffuant, 2006. "Comparing Extremism Propagation Patterns in Continuous Opinion Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(3), pages 1-8.
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    3. Xi Chen & Xiao Zhang & Yong Xie & Wei Li, 2017. "Opinion Dynamics of Social-Similarity-Based Hegselmann–Krause Model," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    4. Haiming Liang & Yucheng Dong & Congcong Li, 2016. "Dynamics of Uncertain Opinion Formation: An Agent-Based Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-1.

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